Optimization simulation and application of welding process of steel pipe in Dianzhong water diversion project based on variational method
Bibliographic record
Abstract
The mechanism study of steel pipe welding in Dianzhong water diversion project is very complicated, and there are many process parameters affecting the temperature distribution of high-frequency heating of welded steel pipe, and the degree of influence and the influence law are not the same.In this paper, Abaqus software is used to carry out the finite element analysis of the steel pipe welding process, and the displacement variational method (i.e., Ritz method) is introduced to derive the radial displacement of the steel pipe when it is subjected to the action of the centralized force, so as to realize the finite element simulation of the welding process of the steel pipe.At the same time, the optimization of the welding process parameters of the steel pipe is realized by combining the radial basis function neural network (RBF) and particle swarm algorithm (PSO).The simulation results show that the Von mise equivalent residual stress at the weld seam reaches the nominal yield strength of the material on both the internal and external surfaces of the steel pipe, while the axial residual stress has a very different distribution law on the internal and external walls of the steel pipe, which belongs to the tensile stress and weld residual compressive stress at the weld seams on the internal and external walls of the steel pipe, which are about 0.4 times the yield strength of the material and 0.7 times the yield strength of the material, respectively.The ring residual stress distribution law of the steel pipe is similar to the axial residual stress, but both reach the nominal yield strength of the material.Through parameter optimization, this paper determines that when the opening angle is 5, the current frequency is 217.35 kHz, and the distance from the coil to the V-point is 252 mm, the corresponding optimization target values are all smaller, and the welding quality of the corresponding weld seam is better.The research in this paper provides a theoretical basis for further improving the welding quality of steel pipe in Dianzhong water diversion project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".